Introduction and objective:Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide. Early detection is crucial for better outcomes. Traditional diagnostic methods, such as imaging and biopsies, often fail to detect HCC in early stages. Liquid biopsy, based on circulating free DNA (cfDNA) analysis, offers a promising, non-invasive approach, allowing frequent testing, addressing tumor heterogeneity, and reducing costs. Review methods:This article's databases were accessed through the WHO website, PubMed, and Google Scholar. A brief description of the state of knowledge:Early detection of HCC significantly improves survival. Biomarkers from cfDNA, including DNA fragment patterns, methylation markers (e.g., USP44), 5-hydroxymethylcytosine (5hmC), and digital PCR analysis, have shown potential in early-stage detection. Advanced cfDNA fragmentomics identifies tumor-specific DNA fragmentation patterns. Techniques like DELFI demonstrate high sensitivity (94%) and specificity (98%). Machine learning enhances cfDNA analysis by integrating multiple markers, improving accuracy in distinguishing cancerous from precancerous states. Combining methylation analysis with machine learning further addresses challenges of tumor heterogeneity. Summary:Studies highlight the high sensitivity and specificity of cfDNA biomarkers for HCC diagnosis, especially in high-risk groups like individuals with cirrhosis. Integrating technologies like 5hmC analysis and machine learning enables early diagnosis and treatment monitoring. These advancements represent a transformative step in cancer diagnostics, offering effective tools to improve patient outcomes.